The present invention relates to a system for compressing and decompressing multidimensional images, and more specifically, to a system capable of efficiently decompressing images in a system in which a pixel equivalence or a frame equivalence is not assured between compression and decompression sides, for example, in an image transmission between different types apparatuses and devices.
There have been conventionally provided many kinds of methods with respect to a compression of image data. For example, there is a method adopting linear (equivalent) quantization means which equally divides a signal level with respect to each sample value of digital image signals, and replaces values included in each range by a representative value. In this method, a data amount from 6-bit (64 gradation) to 8-bit (256 gradation) is required with respect to a generally natural image in order to make the difference between the representative point and an original value be unnoticeable. Accordingly, when the equivalent quantization means records the digitalized image signals as they are, it is necessary to process a large amount of data with respect to each sampled value.
Therefore, when the image signals are coded by a smaller amount of data, there are some attempts. For example, a first attempt uses the human senses of sight and hearing according to which a person is keen of sight with respect to changes in a portion containing subtle signal changes, and according to which a person finds it difficult to detect the changes in a portion containing large signal changes even though there are errors having a predetermined degree. A second attempt transmits few approximate values of original data by using a high adjacent correlation of luminance values of each pixel after an image is divided into a plurality of pixels on the basis of the utilization of time and space bases in the data signals as an object for recording. Furthermore, a third attempt transmits the differences between pixels or frames on the same basis as in the same as the second attempt. A fourth attempt records, transmits and reproduces the image by compressing and decompressing data after reception of the digital data of which a data amount is compressed by adapting several kinds of highly efficient coding methods in which the data amount of each sample decreases by reducing a frequency component based on the fact that the high-frequency component is small. It is well-known that these several attempts have been conventionally performed.
In the above conventional general image data compression methods, since the extreme restoration of divided pixels is treated as being important, there are many cases where pixel numbers coincide with each other between an original image and a restored (decompressed) image. Accordingly, when compression and decompression are performed between images each having different pixel numbers, it is necessary to individually perform an interpolation and a thinning-out of the pixels after the decompression. This means that the conventional image data compression system depends upon a physical image component to a degree because only true and effective data are extracted to be restored.
By the way, when an image taken by such an imaging device is used to a block copy in printing, can be an example is provided wherein a pixel density is quite different between two images having different pixel numbers as described above. A pixel density of an image obtained by an imaging device is "500.times.500" pixels per one image at the most, but a pixel density of an image of an electronic plate-making machine is "several thousands.times.several thousands" pixels per one image, which differs widely from the image obtained by the imaging device. Accordingly, even though the above-mentioned image data compression and decompression system of the pixel equivalence is not applied at all, an alias occurs by a pixel magnification. Furthermore, when an interpolation is performed without taking the above-mentioned pixel magnification, it is impossible to avoid a deterioration of the picture quality by an interpolational distortion because a weight average value of already-known data is appropriated for a broad interpolation area. On the contrary, when the pixel density of the original image is "several thousands.times.several thousands" as described above, since a correlation is extremely high between adjacent pixels, it is theoretically possible to extremely compress the image data. However, the conventional image data compression and decompression system has the problem that it is impossible to increase a compression ratio because the conventional system has the condition that pixel numbers coincide with each other between the above-mentioned original image and restored (decompressed) image.